LLM-Assisted Codebase Analysis for Migration: Comparing Codex, Claude, and VS Code Agents
This article explores how large language model (LLM) tools like Codex, Claude, and VS Code Copilot can assist in migrating legacy systems to new technology stacks by providing interactive codebase analysis and summarization.
Why it matters
This article provides insights into how LLM tools can assist in understanding and migrating legacy systems, which is a common challenge faced by many organizations.
Key Points
- 1Legacy systems often fail due to incomplete understanding of system behavior and undocumented requirements
- 2LLM agents can help explore codebases, summarize structure, and trace important flows instead of manual reverse engineering
- 3Using the full codebase context in LLMs can lead to issues like signal dilution, less reasoning space, and slow iteration
- 4The article uses the eShopOnWeb codebase as an example to compare Codex, Claude, and VS Code Copilot agents
Details
The article discusses how the distribution of engineering effort shifts when using LLM agents, with less time spent on writing code and more time on validation, specs, design, and review. While agents can generate code, they cannot guarantee domain correctness, and most real bugs are due to misunderstood requirements, missing system context, and edge cases. The article also explores the downsides of using the full codebase context, such as signal dilution, less reasoning space, noise bias, and slow iteration. The eShopOnWeb codebase is used as an example, which is a moderate-complexity .NET web application with a public API, Blazor admin UI, and multiple test projects. The intent is to use this codebase to evaluate different LLM-assisted migration approaches, including using Codex, Claude Code, and VS Code Copilot.
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